1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
3/12/2022 Electricidad 24773 Andrés es del mes pasado
4/12/2022 Comida 30844 Tami Uber Eats cumpleaños
4/12/2022 Comida 7190 Tami Queso cabra laminado
11/12/2022 Comida 56044 Tami NA
12/12/2022 Diosi 20990 Tami Antiparasitario
12/12/2022 Gaviscón y Paracetamol 12040 Tami NA
12/12/2022 Diosi 16500 Tami Pack Dental Life
19/12/2022 Bencina + Tag cumple Delox 15000 Tami NA
19/12/2022 Plata Reciclaje y Basurero 20000 Tami NA
19/12/2022 Comida 71002 Tami NA
25/12/2022 VTR 21990 Andrés NA
25/12/2022 Comida 87705 Andrés Lider
27/12/2022 Netflix 8320 Tami NA
28/12/2022 Electricidad 52000 Andrés atrasado ENEL
29/12/2022 Regalo Matri Cony 69990 Tami NA
3/1/2023 Comida 32425 Tami NA
6/1/2023 Uber Matri Cony 9709 Tami NA
9/1/2023 Comida 53612 Tami NA
17/1/2023 Gas 70300 Andrés NA
17/1/2023 Comida 28624 Andrés NA
21/1/2023 Comida 71924 Tami NA
22/1/2023 Remedios Covid 50000 Tami NA
27/1/2023 Comida 13580 Andrés NA
27/1/2023 Comida 12024 Tami NA
27/1/2023 Comida 24640 Tami Caramagnola
28/1/2023 Netflix 8320 Tami NA
26/1/2023 Comida 10370 Andrés NA
29/1/2023 Comida 59172 Tami NA
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
  tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
    theme_bw()+ labs(x="Weeks")

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 5.3870e+08   2    5.7812 0.0033 ** 
## lag_depvar    7.9624e+10   1 1709.0019 <2e-16 ***
## Residuals     2.5113e+10 539                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff       lwr      upr     p adj
## 1-0  7228.838  1009.031 13448.65 0.0178595
## 2-0 27739.767 22036.550 33442.98 0.0000000
## 2-1 20510.929 17091.022 23930.84 0.0000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
## 24   22529.57             0   21782.57
## 25   24642.71             0   22529.57
## 26   17692.29             0   24642.71
## 27   19668.29             0   17692.29
## 28   28640.00             0   19668.29
## 29   28706.00             0   28640.00
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## 232  74875.14             2   80355.00
## 233  81347.00             2   74875.14
## 234  66062.43             2   81347.00
## 235  56946.43             2   66062.43
## 236  47732.14             2   56946.43
## 237  38129.71             2   47732.14
## 238  42928.29             2   38129.71
## 239  45392.57             2   42928.29
## 240  37895.43             2   45392.57
## 241  30660.29             2   37895.43
## 242  42430.86             2   30660.29
## 243  35845.14             2   42430.86
## 244  40350.43             2   35845.14
## 245  31494.71             2   40350.43
## 246  30013.29             2   31494.71
## 247  34197.57             2   30013.29
## 248  37430.14             2   34197.57
## 249  26932.43             2   37430.14
## 250  33729.86             2   26932.43
## 251  38081.43             2   33729.86
## 252  44028.00             2   38081.43
## 253  47139.71             2   44028.00
## 254  46558.86             2   47139.71
## 255  58350.57             2   46558.86
## 256  78380.00             2   58350.57
## 257  78168.29             2   78380.00
## 258  70510.86             2   78168.29
## 259  72207.14             2   70510.86
## 260  67881.00             2   72207.14
## 261  69536.43             2   67881.00
## 262  62390.71             2   69536.43
## 263  50113.14             2   62390.71
## 264  45565.57             2   50113.14
## 265  45805.29             2   45565.57
## 266  41348.57             2   45805.29
## 267  51426.86             2   41348.57
## 268  47160.57             2   51426.86
## 269  51907.43             2   47160.57
## 270  49751.43             2   51907.43
## 271  54407.43             2   49751.43
## 272  54746.29             2   54407.43
## 273  61634.57             2   54746.29
## 274  58926.43             2   61634.57
## 275  69999.29             2   58926.43
## 276  63044.86             2   69999.29
## 277  63285.29             2   63044.86
## 278  61395.43             2   63285.29
## 279  67969.43             2   61395.43
## 280  60792.57             2   67969.43
## 281  56859.14             2   60792.57
## 282  44899.43             2   56859.14
## 283  43064.14             2   44899.43
## 284  62790.29             2   43064.14
## 285  69120.71             2   62790.29
## 286  69589.43             2   69120.71
## 287  66633.29             2   69589.43
## 288  65588.57             2   66633.29
## 289  70168.57             2   65588.57
## 290  74644.71             2   70168.57
## 291  52891.00             2   74644.71
## 292  41560.57             2   52891.00
## 293  34704.86             2   41560.57
## 294  46520.00             2   34704.86
## 295  50231.00             2   46520.00
## 296  49216.71             2   50231.00
## 297  76914.86             2   49216.71
## 298  83720.71             2   76914.86
## 299  84485.00             2   83720.71
## 300  89765.00             2   84485.00
## 301  87702.86             2   89765.00
## 302  82013.86             2   87702.86
## 303  85982.43             2   82013.86
## 304  57248.43             2   85982.43
## 305  52968.43             2   57248.43
## 306  52601.86             2   52968.43
## 307  45493.29             2   52601.86
## 308  42298.86             2   45493.29
## 309  46423.71             2   42298.86
## 310  37898.00             2   46423.71
## 311  36435.14             2   37898.00
## 312  30209.57             2   36435.14
## 313  34541.86             2   30209.57
## 314  33604.71             2   34541.86
## 315  37990.71             2   33604.71
## 316  35683.43             2   37990.71
## 317  65201.86             2   35683.43
## 318  62730.57             2   65201.86
## 319  64589.14             2   62730.57
## 320  73744.86             2   64589.14
## 321  76477.71             2   73744.86
## 322 105647.43             2   76477.71
## 323 103790.29             2  105647.43
## 324  76122.29             2  103790.29
## 325  74746.14             2   76122.29
## 326  72865.71             2   74746.14
## 327  63652.57             2   72865.71
## 328  60358.29             2   63652.57
## 329  25957.14             2   60358.29
## 330  30178.43             2   25957.14
## 331  30681.57             2   30178.43
## 332  33337.29             2   30681.57
## 333  32582.71             2   33337.29
## 334  39184.43             2   32582.71
## 335  40415.71             2   39184.43
## 336  34975.43             2   40415.71
## 337  34076.14             2   34975.43
## 338  34221.14             2   34076.14
## 339  28862.57             2   34221.14
## 340  35729.86             2   28862.57
## 341  36489.29             2   35729.86
## 342  36785.14             2   36489.29
## 343  37787.71             2   36785.14
## 344  39832.14             2   37787.71
## 345  41917.86             2   39832.14
## 346  41633.57             2   41917.86
## 347  33557.00             2   41633.57
## 348  22759.57             2   33557.00
## 349  28877.86             2   22759.57
## 350  27574.00             2   28877.86
## 351  27104.71             2   27574.00
## 352  24376.14             2   27104.71
## 353  29732.29             2   24376.14
## 354  34030.00             2   29732.29
## 355  39139.71             2   34030.00
## 356  37066.57             2   39139.71
## 357  38509.29             2   37066.57
## 358  40957.29             2   38509.29
## 359  49423.00             2   40957.29
## 360  50053.29             2   49423.00
## 361  50284.14             2   50053.29
## 362  53103.86             2   50284.14
## 363  50223.00             2   53103.86
## 364  49587.14             2   50223.00
## 365  41167.71             2   49587.14
## 366  37958.71             2   41167.71
## 367  33582.29             2   37958.71
## 368  31039.43             2   33582.29
## 369  26526.57             2   31039.43
## 370  34869.43             2   26526.57
## 371  37487.43             2   34869.43
## 372  46514.43             2   37487.43
## 373  39613.43             2   46514.43
## 374  38980.57             2   39613.43
## 375  37306.14             2   38980.57
## 376  36771.29             2   37306.14
## 377  26317.00             2   36771.29
## 378  31580.71             2   26317.00
## 379  23626.57             2   31580.71
## 380  33035.71             2   23626.57
## 381  44864.57             2   33035.71
## 382  48946.14             2   44864.57
## 383  46969.57             2   48946.14
## 384  49249.57             2   46969.57
## 385  56370.14             2   49249.57
## 386  67228.71             2   56370.14
## 387  59457.29             2   67228.71
## 388  53124.71             2   59457.29
## 389  52814.14             2   53124.71
## 390  61262.00             2   52814.14
## 391  61861.14             2   61262.00
## 392  71784.71             2   61861.14
## 393  59313.29             2   71784.71
## 394  61107.00             2   59313.29
## 395  60603.43             2   61107.00
## 396  60012.57             2   60603.43
## 397  58280.43             2   60012.57
## 398  56862.71             2   58280.43
## 399  41704.43             2   56862.71
## 400  51533.00             2   41704.43
## 401  50388.71             2   51533.00
## 402  49205.29             2   50388.71
## 403  56533.29             2   49205.29
## 404  47996.14             2   56533.29
## 405  47207.57             2   47996.14
## 406  45292.00             2   47207.57
## 407  40343.43             2   45292.00
## 408  39004.86             2   40343.43
## 409  36788.43             2   39004.86
## 410  30027.57             2   36788.43
## 411  39040.14             2   30027.57
## 412  42390.14             2   39040.14
## 413  36291.14             2   42390.14
## 414  30668.29             2   36291.14
## 415  47693.00             2   30668.29
## 416  52094.43             2   47693.00
## 417  56592.57             2   52094.43
## 418  47971.43             2   56592.57
## 419  43762.43             2   47971.43
## 420  42246.71             2   43762.43
## 421  46352.43             2   42246.71
## 422  33094.86             2   46352.43
## 423  32784.86             2   33094.86
## 424  26212.43             2   32784.86
## 425  32611.57             2   26212.43
## 426  42144.86             2   32611.57
## 427  50034.86             2   42144.86
## 428  46332.00             2   50034.86
## 429  42976.29             2   46332.00
## 430  39456.29             2   42976.29
## 431  39328.29             2   39456.29
## 432  35296.14             2   39328.29
## 433  30875.43             2   35296.14
## 434  27709.00             2   30875.43
## 435  29513.29             2   27709.00
## 436  31630.43             2   29513.29
## 437  29346.14             2   31630.43
## 438  34916.86             2   29346.14
## 439  42020.86             2   34916.86
## 440  38303.00             2   42020.86
## 441  37966.43             2   38303.00
## 442  41408.14             2   37966.43
## 443  38988.14             2   41408.14
## 444  43555.29             2   38988.14
## 445  38114.00             2   43555.29
## 446  27847.86             2   38114.00
## 447  26517.00             2   27847.86
## 448  39518.29             2   26517.00
## 449  39153.71             2   39518.29
## 450  45623.14             2   39153.71
## 451  40627.43             2   45623.14
## 452  41027.71             2   40627.43
## 453  42882.86             2   41027.71
## 454  47139.43             2   42882.86
## 455  35547.57             2   47139.43
## 456  41099.00             2   35547.57
## 457  35859.57             2   41099.00
## 458  44524.57             2   35859.57
## 459  48554.29             2   44524.57
## 460  51554.29             2   48554.29
## 461  47810.29             2   51554.29
## 462  50490.00             2   47810.29
## 463  50720.71             2   50490.00
## 464  52720.71             2   50720.71
## 465  52145.57             2   52720.71
## 466  55515.57             2   52145.57
## 467  52457.00             2   55515.57
## 468  58239.57             2   52457.00
## 469  50523.57             2   58239.57
## 470  47788.57             2   50523.57
## 471  46170.00             2   47788.57
## 472  42305.57             2   46170.00
## 473  46605.57             2   42305.57
## 474  55149.57             2   46605.57
## 475  48769.57             2   55149.57
## 476  50719.43             2   48769.57
## 477  44753.71             2   50719.43
## 478  42898.00             2   44753.71
## 479  46141.14             2   42898.00
## 480  34022.57             2   46141.14
## 481  26651.86             2   34022.57
## 482  28791.86             2   26651.86
## 483  31879.00             2   28791.86
## 484  33584.71             2   31879.00
## 485  34690.43             2   33584.71
## 486  27410.43             2   34690.43
## 487  41755.00             2   27410.43
## 488  49379.57             2   41755.00
## 489  57198.86             2   49379.57
## 490  51144.57             2   57198.86
## 491  56677.43             2   51144.57
## 492  65416.43             2   56677.43
## 493  69779.71             2   65416.43
## 494  54046.00             2   69779.71
## 495  43259.57             2   54046.00
## 496  40998.57             2   43259.57
## 497  41368.57             2   40998.57
## 498  42274.29             2   41368.57
## 499  35962.71             2   42274.29
## 500  38709.00             2   35962.71
## 501  44778.14             2   38709.00
## 502  51282.43             2   44778.14
## 503  52094.86             2   51282.43
## 504  52221.43             2   52094.86
## 505  45011.43             2   52221.43
## 506  46545.43             2   45011.43
## 507  42263.00             2   46545.43
## 508  45417.43             2   42263.00
## 509  45034.71             2   45417.43
## 510  37840.57             2   45034.71
## 511  39135.43             2   37840.57
## 512  38191.14             2   39135.43
## 513  39456.86             2   38191.14
## 514  42479.14             2   39456.86
## 515  34282.57             2   42479.14
## 516  28878.43             2   34282.57
## 517  56227.14             2   28878.43
## 518  65569.43             2   56227.14
## 519  69751.29             2   65569.43
## 520  62171.71             2   69751.29
## 521  63705.14             2   62171.71
## 522  79257.86             2   63705.14
## 523  87244.71             2   79257.86
## 524  58568.00             2   87244.71
## 525  52695.29             2   58568.00
## 526  48911.00             2   52695.29
## 527  53924.00             2   48911.00
## 528  53358.86             2   53924.00
## 529  42121.14             2   53358.86
## 530  47835.71             2   42121.14
## 531  62329.29             2   47835.71
## 532  56056.86             2   62329.29
## 533  59946.43             2   56056.86
## 534  64511.57             2   59946.43
## 535  61137.43             2   64511.57
## 536  55448.71             2   61137.43
## 537  47964.43             2   55448.71
## 538  46425.71             2   47964.43
## 539  55512.00             2   46425.71
## 540  55226.29             2   55512.00
## 541  46709.14             2   55226.29
## 542  49254.71             2   46709.14
## 543  49056.29             2   49254.71
## 544  49850.57             2   49056.29
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   387 49974.03 15546.826
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57  42274.29
## [498]  35962.71  38709.00  44778.14  51282.43  52094.86  52221.43  45011.43
## [505]  46545.43  42263.00  45417.43  45034.71  37840.57  39135.43  38191.14
## [512]  39456.86  42479.14  34282.57  28878.43  56227.14  65569.43  69751.29
## [519]  62171.71  63705.14  79257.86  87244.71  58568.00  52695.29  48911.00
## [526]  53924.00  53358.86  42121.14  47835.71  62329.29  56056.86  59946.43
## [533]  64511.57  61137.43  55448.71  47964.43  46425.71  55512.00  55226.29
## [540]  46709.14  49254.71  49056.29  49850.57
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##             2             3             4             5             6 
##   2010.254650   4036.673269   -534.365450   2441.284702  -2962.302523 
##             7             8             9            10            11 
##    521.410016  -5651.931519  -1192.119901  -3970.895863   -427.277865 
##            12            13            14            15            16 
##  -4947.685141  -1622.968947   -913.207628    365.188014  -3252.217591 
##            17            18            19            20            21 
##   -390.101259  -2140.501375   6592.682337  -1528.672740  -1209.361439 
##            22            23            24            25            26 
##   1473.642658  -1185.358657    234.735355   1696.219521  -7097.646678 
##            27            28            29            30            31 
##    941.682887   8189.598547    428.964560     -3.040282  -2390.115166 
##            32            33            34            35            36 
##   1582.670165   4581.431808   1142.494077   2407.686836  -1849.281216 
##            37            38            39            40            41 
##   4622.431841   4312.312062  -2261.396099  -2972.231149  -1106.465831 
##            42            43            44            45            46 
## -10739.812800   7274.800754   2555.474596   1369.173528   8109.312740 
##            47            48            49            50            51 
##    701.971379   6543.698420   6737.300440  -5852.184534  -4777.777001 
##            52            53            54            55            56 
##  -5051.358016  -7928.713106   6117.932753  -4077.774458  -4901.363646 
##            57            58            59            60            61 
##   3842.408595    881.924322    -35.905315    138.918230  -4999.055126 
##            62            63            64            65            66 
##  18117.092151   3659.318327  -3624.268175   5939.138755   7365.083269 
##            67            68            69            70            71 
##  14668.431167   1741.112967 -13168.086358  -1286.570473   4658.878294 
##            72            73            74            75            76 
##  -4879.760094  -4393.685307 -10494.281784   2454.437319  -5406.665771 
##            77            78            79            80            81 
##   1050.057301  -6876.442542    528.194185  -2369.911354  -2710.492045 
##            82            83            84            85            86 
##  -3950.118195   -558.972739   2295.288240   3749.303142    470.757786 
##            87            88            89            90            91 
##   -489.188042    191.887166   4298.138408  -1160.020902   1151.830235 
##            92            93            94            95            96 
##  -2061.872729  -1044.955097    175.562311    273.350198  -7484.817523 
##            97            98            99           100           101 
##   2380.383192  -8608.817416  -2958.247024  -4058.800942  -1759.061900 
##           102           103           104           105           106 
##  -1282.980082   3160.561058  -2355.104554   2579.419528  -1167.304181 
##           107           108           109           110           111 
##    961.152231   2580.470670  -3156.360874  -4729.185422   -862.241449 
##           112           113           114           115           116 
##   1891.999184  11686.348095  -1232.003595   2676.104553   4273.427068 
##           117           118           119           120           121 
##   3518.211785  -1081.162410  -4701.324212  -3717.572695   2320.313678 
##           122           123           124           125           126 
##  -1728.660228   1341.603253   8861.377877    862.742197    145.504131 
##           127           128           129           130           131 
##  -2507.752255   2663.421752   7063.806816   1032.607729  -8480.168092 
##           132           133           134           135           136 
##   1753.927536   4142.292265  -3151.953317  -1413.661813   -850.574939 
##           137           138           139           140           141 
##  -3878.117772   1179.275540   -496.926718  -2915.442315   1712.514490 
##           142           143           144           145           146 
##  -1883.421116  -7833.922211   2024.317245  -3489.908162   2088.418116 
##           147           148           149           150           151 
##   -266.468236   1014.855314   -364.923871   1346.821198   1183.930598 
##           152           153           154           155           156 
##   3355.975054  -4857.441893  -1177.555649  -3240.097965   5948.428284 
##           157           158           159           160           161 
##   9748.010043  -3215.492394  -4564.452370   3813.839706    412.895581 
##           162           163           164           165           166 
##   2916.550650  -5684.942120  -6529.104375   4367.683619  17611.477599 
##           167           168           169           170           171 
##   3864.010688   -160.502213  -2210.495205   -872.988709   3819.277122 
##           172           173           174           175           176 
##      3.277769  -7845.591428   3083.825745   4548.494345    852.450332 
##           177           178           179           180           181 
##   8976.880812  -9014.424040  -3249.078593 -10526.065201 -11032.528720 
##           182           183           184           185           186 
##   1432.477327   9494.762826  -1217.507327   6140.014233   6771.879873 
##           187           188           189           190           191 
##  13378.387265   8657.682503  -3835.756150   2682.298042  10582.702331 
##           192           193           194           195           196 
##  -1427.153883  -2234.699618 -10076.547542  -6169.065502   1422.999483 
##           197           198           199           200           201 
##  -5042.190925  -9607.536537   5567.437681  -2877.595927  -1521.865759 
##           202           203           204           205           206 
##   -612.760220   6686.841852  10077.903087    776.938358   3121.250846 
##           207           208           209           210           211 
##   3294.251655   5980.598085  13031.218208  -5485.256963 -11099.273980 
##           212           213           214           215           216 
##  -5476.041651 -10399.000830  -4890.327358   1712.041274 -12821.469406 
##           217           218           219           220           221 
##  16574.584117   8004.764920   1726.502702  26885.988195  12731.674924 
##           222           223           224           225           226 
##   7540.151943  14229.688124  -3709.901698  -1543.436533   3971.282938 
##           227           228           229           230           231 
##    553.916064   2939.464525   9199.384347   6030.622328  -1701.623955 
##           232           233           234           235           236 
##  -1624.623309   9627.684347 -11302.726378  -7084.959563  -8346.741089 
##           237           238           239           240           241 
##  -9910.924188   3264.496014   1542.662885  -8104.243296  -8799.120690 
##           242           243           244           245           246 
##   9283.155858  -7570.825055   2679.625253 -10106.354885  -3862.344125 
##           247           248           249           250           251 
##   1614.292062   1196.627679 -12121.077592   3834.218116   2255.932241 
##           252           253           254           255           256 
##   4406.333199   2330.450492   -964.965766  11333.469545  21076.187143 
##           257           258           259           260           261 
##   3391.445807  -4081.290055   4295.089207  -1510.838561   3918.577416 
##           262           263           264           265           266 
##  -4671.279328 -10715.160236  -4552.174600   -345.305770  -5011.139062 
##           267           268           269           270           271 
##   8955.040358  -4103.216544   4365.410565  -1931.594405   4605.230411 
##           272           273           274           275           276 
##    882.343423   7475.021102  -1242.239897  12193.113671  -4420.918103 
##           277           278           279           280           281 
##   1886.329504   -213.269765   8009.380613  -4902.421959  -2574.991987 
##           282           283           284           285           286 
## -11103.310135  -2505.327098  18821.859784   7943.837974   2890.090684 
##           287           288           289           290           291 
##   -474.943388   1059.185947   6550.560972   7031.259646 -18627.297204 
##           292           293           294           295           296 
## -10980.487518  -7951.901600   9843.945137   3247.795834  -1003.846511 
##           297           298           299           300           301 
##  27579.126468  10222.017720   5049.093317   9662.355127   2994.110678 
##           302           303           304           305           306 
##   -895.942448   8035.528973 -24160.524659  -3373.910428     -6.747991 
##           307           308           309           310           311 
##  -6795.534336  -3788.674617   3122.898913  -9001.207617  -3026.506778 
##           312           313           314           315           316 
##  -7975.928848   1787.344455  -2929.144638   2274.388545  -3859.102015 
##           317           318           319           320           321 
##  27672.128148   -550.081966   3464.359361  10998.715917   5744.423411 
##           322           323           324           325           326 
##  32530.081326   5226.221158 -20821.667352   1938.860026   1258.934069 
##           327           328           329           330           331 
##  -6313.783592  -1570.820825 -33098.135146   1133.597325  -2045.773236 
##           332           333           334           335           336 
##    171.015460  -2900.315401   4359.662646   -168.174117  -6682.593749 
##           337           338           339           340           341 
##  -2835.949788  -1906.441955  -7391.506703   4150.423781  -1080.946068 
##           342           343           344           345           346 
##  -1447.589901   -703.114695    466.702920    768.923702  -1334.871840 
##           347           348           349           350           351 
##  -9163.441587 -12915.129910   2622.484011  -4018.768126  -3350.610949 
##           352           353           354           355           356 
##  -5669.792667   2066.667842   1691.855965   3052.383013  -3478.309674 
##           357           358           359           360           361 
##   -227.052469    962.370151   7292.528244    537.598040    218.614268 
##           362           363           364           365           366 
##   2836.936233  -2503.748649   -626.438998  -8491.166319  -4355.328565 
##           367           368           369           370           371 
##  -5932.329104  -4657.331208  -6951.881848   5327.846217    667.806905 
##           372           373           374           375           376 
##   7410.948199  -7364.915261  -1977.562728  -3099.907153  -2174.046851 
##           377           378           379           380           381 
## -12161.740456   2221.955282 -10324.082036   6023.998336   9644.622924 
##           382           383           384           385           386 
##   3407.081149  -2130.121464   1874.175657   7005.748671  11652.563397 
##           387           388           389           390           391 
##  -5591.532436  -5144.560396     69.199110   8787.988744   2017.493678 
##           392           393           394           395           396 
##  11418.392215  -9710.039794   2963.346271    894.996390    743.438709 
##           397           398           399           400           401 
##   -473.259446   -379.908207 -14301.425734   8750.744929   -967.669290 
##           402           403           404           405           406 
##  -1152.859935   7207.524956  -7722.328595  -1063.372125  -2291.019290 
##           407           408           409           410           411 
##  -5568.508028  -2590.105454  -3638.807512  -8466.123903   6444.401095 
##           412           413           414           415           416 
##   1932.124655  -7089.307226  -7391.593610  14538.319774   4087.937063 
##           417           418           419           420           421 
##   4746.415687  -7798.761824  -4486.955037  -2330.873565   3097.100945 
##           422           423           424           425           426 
## -13742.163118  -2486.685682  -8788.680258   3344.037164   7294.917177 
##           427           428           429           430           431 
##   6868.386400  -3717.452098  -3842.913320  -4435.496480  -1492.762086 
##           432           433           434           435           436 
##  -5413.241874  -6316.444866  -5626.384970  -1059.809164   -516.666932 
##           437           438           439           440           441 
##  -4647.879745   2915.571674   5159.860322  -4755.297145  -1848.529993 
##           442           443           444           445           446 
##   1886.798345  -3535.642119   3142.630634  -6282.883108 -11802.223921 
##           447           448           449           450           451 
##  -4177.229368   9985.053165  -1721.420313   5066.048606  -5573.386369 
##           452           453           454           455           456 
##   -815.000745    690.945774   3329.150447 -11976.002232   3687.788392 
##           457           458           459           460           461 
##  -6394.527297   6841.181090   3311.829032   2796.436346  -3564.666831 
##           462           463           464           465           466 
##   2381.192219    274.210248   2072.942551   -246.935757   3624.800309 
##           467           468           469           470           471 
##  -2373.650355   6077.120075  -6683.408611  -2687.219240  -1919.864939 
##           472           473           474           475           476 
##  -4372.304034   3298.898730   8091.717510  -5741.792339   1773.770894 
##           477           478           479           480           481 
##  -5892.935833  -2544.353515   2317.654593 -12630.129994  -9428.993684 
##           482           483           484           485           486 
##   -859.020425    361.255499   -626.154009  -1008.449815  -9253.039272 
##           487           488           489           490           491 
##  11442.369200   6553.199475   7721.055153  -5154.523057   5659.897545 
##           492           493           494           495           496 
##   9572.211543   6311.875702 -13228.228219 -10289.072242  -3140.340080 
##           497           498           499           500           501 
##   -797.916652   -214.978425  -7316.665765    935.631162   4609.002996 
##           502           503           504           505           506 
##   5818.764359    957.063995    374.898958  -6945.517871    878.253431 
##           507           508           509           510           511 
##  -4742.387231   2147.893813   -986.642151  -7846.917417   -276.122232 
##           512           513           514           515           516 
##  -2349.999527   -260.520860   1657.596561  -9175.519382  -7429.237864 
##           517           518           519           520           521 
##  24633.876236  10118.026268   6149.974869  -5077.713765   3067.888296 
##           522           523           524           525           526 
##  17282.888986  11702.060139 -23942.130548  -4798.204812  -3459.324121 
##           527           528           529           530           531 
##   4854.964601    -83.357665 -10828.059561   4689.931121  14198.294869 
##           532           533           534           535           536 
##  -4717.857647   4643.578029   5815.584305  -1541.041844  -4286.262799 
##           537           538           539           540           541 
##  -6807.897770  -1817.562749   8611.047648    398.750958  -7869.143977 
##           542           543           544 
##   2106.507995   -312.594934    654.793462 
## 
## $fitted.values
##        2        3        4        5        6        7        8        9 
## 17259.03 20102.33 24350.51 24068.86 26419.02 23755.30 24470.65 19709.26 
##       10       11       12       13       14       15       16       17 
## 19446.18 16792.56 17568.97 14302.83 14353.92 15017.67 16711.93 15034.24 
##       18       19       20       21       22       23       24       25 
## 16067.50 15441.89 22514.67 21599.93 21080.50 22967.93 22294.84 22946.49 
##       26       27       28       29       30       31       32       33 
## 24789.93 18726.60 20450.40 28277.04 28334.61 28007.97 25640.62 27041.14 
##       34       35       36       37       38       39       40       41 
## 30878.93 31226.88 32634.14 30148.14 34130.69 37334.40 34394.52 31209.75 
##       42       43       44       45       46       47       48       49 
## 30059.10 20651.48 28159.95 30593.11 31680.83 38509.60 38004.87 42660.70 
##       50       51       52       53       54       55       56       57 
## 46891.18 39599.06 34174.93 29204.43 22358.21 28639.63 25224.94 21527.59 
##       58       59       60       61       62       63       64       65 
## 25929.93 27187.76 27484.37 27895.63 23772.19 40340.82 42182.27 37434.72 
##       66       67       68       69       70       71       72       73 
## 41635.92 46544.85 57198.46 55214.94 40478.28 37987.55 41001.33 35309.26 
##       74       75       76       77       78       79       80       81 
## 30767.71 21483.85 24680.95 20612.23 22695.44 17597.95 19610.63 18838.21 
##       82       83       84       85       86       87       88       89 
## 17867.26 15938.83 17214.85 20817.98 25229.67 26218.19 26243.11 26859.00 
##       90       91       92       93       94       95       96       97 
## 30978.45 29810.60 30808.59 28875.67 28076.58 28444.22 28850.25 22436.47 
##       98       99      100      101      102      103      104      105 
## 25447.39 18487.39 17345.09 15388.49 15687.84 16364.30 20830.82 19915.58 
##      106      107      108      109      110      111      112      113 
## 23421.88 23212.13 24885.96 27758.79 25260.33 21708.67 21983.72 24626.37 
##      114      115      116      117      118      119      120      121 
## 35476.00 33671.32 35506.29 38500.50 40453.73 38145.32 32973.43 29319.83 
##      122      123      124      125      126      127      128      129 
## 31399.80 29682.11 30862.05 38451.40 38094.35 37157.18 34025.01 35803.76 
##      130      131      132      133      134      135      136      137 
## 41194.25 40635.31 31849.07 33112.14 36297.52 32713.09 31102.57 30188.83 
##      138      139      140      141      142      143      144      145 
## 26750.58 28163.07 27933.01 25622.49 27644.14 26270.78 19881.68 22908.05 
##      146      147      148      149      150      151      152      153 
## 20737.72 23710.75 24250.00 25838.21 26020.04 27671.93 28970.88 31998.87 
##      154      155      156      157      158      159      160      161 
## 27475.27 26739.24 24297.86 30183.85 41235.92 39568.45 36937.02 41950.39 
##      162      163      164      165      166      167      168      169 
## 43357.02 46768.23 42240.39 37554.03 42971.81 59251.56 61460.65 59876.92 
##      170      171      172      173      174      175      176      177 
## 56706.99 55108.44 57807.29 56832.73 49135.46 51955.08 55692.55 55728.69 
##      178      179      180      181      182      183      184      185 
## 62847.71 53363.08 50118.49 40939.81 32490.81 35994.24 46083.79 45540.56 
##      186      187      188      189      190      191      192      193 
## 51485.12 57222.18 67990.32 73265.90 66969.27 67162.44 74223.01 69905.41 
##      194      195      196      197      198      199      200      201 
## 65434.40 54693.07 48731.43 50153.76 45754.54 37934.13 44350.02 42579.87 
##      202      203      204      205      206      207      208      209 
## 42218.33 42696.02 49480.67 58357.63 57987.75 59710.18 61363.69 65149.64 
##      210      211      212      213      214      215      216      217 
## 74603.11 66696.85 54902.18 49518.43 40527.18 37489.10 40598.47 30632.42 
##      218      219      220      221      222      223      224      225 
## 47582.52 54893.21 55793.87 78527.90 86012.56 88013.03 95593.90 86557.29 
##      226      227      228      229      230      231      232      233 
## 80564.00 80146.51 76801.11 75963.76 80694.23 82056.62 76499.77 71719.32 
##      234      235      236      237      238      239      240      241 
## 77365.15 64031.39 56078.88 48040.64 39663.79 43849.91 45999.67 39459.41 
##      242      243      244      245      246      247      248      249 
## 33147.70 43415.97 37670.80 41601.07 33875.63 32583.28 36233.52 39053.51 
##      250      251      252      253      254      255      256      257 
## 29895.64 35825.50 39621.67 44809.26 47523.82 47017.10 57303.81 74776.84 
##      258      259      260      261      262      263      264      265 
## 74592.15 67912.05 69391.84 65617.85 67061.99 60828.30 50117.75 46150.59 
##      266      267      268      269      270      271      272      273 
## 46359.71 42471.82 51263.79 47542.02 51683.02 49802.20 53863.94 54159.55 
##      274      275      276      277      278      279      280      281 
## 60168.67 57806.17 67465.78 61398.96 61608.70 59960.05 65694.99 59434.13 
##      282      283      284      285      286      287      288      289 
## 56002.74 45569.47 43968.43 61176.88 66699.34 67108.23 64529.39 63618.01 
##      290      291      292      293      294      295      296      297 
## 67613.45 71518.30 52541.06 42656.76 36676.05 46983.20 50220.56 49335.73 
##      298      299      300      301      302      303      304      305 
## 73498.70 79435.91 80102.64 84708.75 82909.80 77946.90 81408.95 56342.34 
##      306      307      308      309      310      311      312      313 
## 52608.61 52288.82 46087.53 43300.82 46899.21 39461.65 38185.50 32754.51 
##      314      315      316      317      318      319      320      321 
## 36533.86 35716.33 39542.53 37529.73 63280.65 61124.78 62746.14 70733.29 
##      322      323      324      325      326      327      328      329 
## 73117.35 98564.06 96943.95 72807.28 71606.78 69966.36 61929.11 59055.28 
##      330      331      332      333      334      335      336      337 
## 29044.83 32727.34 33166.27 35483.03 34824.77 40583.89 41658.02 36912.09 
##      338      339      340      341      342      343      344      345 
## 36127.58 36254.08 31579.43 37570.23 38232.73 38490.83 39365.44 41148.93 
##      346      347      348      349      350      351      352      353 
## 42968.44 42720.44 35674.70 26255.37 31592.77 30455.33 30045.94 27665.62 
##      354      355      356      357      358      359      360      361 
## 32338.14 36087.33 40544.88 38736.34 39994.92 42130.47 49515.69 50065.53 
##      362      363      364      365      366      367      368      369 
## 50266.92 52726.75 50213.58 49658.88 42314.04 39514.61 35696.76 33478.45 
##      370      371      372      373      374      375      376      377 
## 29541.58 36819.62 39103.48 46978.34 40958.13 40406.05 38945.33 38478.74 
##      378      379      380      381      382      383      384      385 
## 29358.76 33950.65 27011.72 35219.95 45539.06 49099.69 47375.40 49364.39 
##      386      387      388      389      390      391      392      393 
## 55576.15 65048.82 58269.27 52744.94 52474.01 59843.65 60366.32 69023.33 
##      394      395      396      397      398      399      400      401 
## 58143.65 59708.43 59269.13 58753.69 57242.62 56005.85 42782.26 51356.38 
##      402      403      404      405      406      407      408      409 
## 50358.15 49325.76 55718.47 48270.94 47583.02 45911.94 41594.96 40427.24 
##      410      411      412      413      414      415      416      417 
## 38493.70 32595.74 40458.02 43380.45 38059.88 33154.68 48006.49 51846.16 
##      418      419      420      421      422      423      424      425 
## 55770.19 48249.38 44577.59 43255.33 46837.02 35271.54 35001.11 29267.53 
##      426      427      428      429      430      431      432      433 
## 34849.94 43166.47 50049.45 46819.20 43891.78 40821.05 40709.38 37191.87 
##      434      435      436      437      438      439      440      441 
## 33335.38 30573.09 32147.10 33994.02 32001.29 36861.00 43058.30 39814.96 
##      442      443      444      445      446      447      448      449 
## 39521.34 42523.78 40412.66 44396.88 39650.08 30694.23 29533.23 40875.13 
##      450      451      452      453      454      455      456      457 
## 40557.09 46200.81 41842.72 42191.91 43810.28 47523.57 37411.21 42254.10 
##      458      459      460      461      462      463      464      465 
## 37683.39 45242.46 48757.85 51374.95 48108.81 50446.50 50647.77 52392.51 
##      466      467      468      469      470      471      472      473 
## 51890.77 54830.65 52162.45 57206.98 50475.79 48089.86 46677.88 43306.67 
##      474      475      476      477      478      479      480      481 
## 47057.85 54511.36 48945.66 50646.65 45442.35 43823.49 46652.70 36080.85 
##      482      483      484      485      486      487      488      489 
## 29650.88 31517.74 34210.87 35698.88 36663.47 30312.63 42826.37 49477.80 
##      490      491      492      493      494      495      496      497 
## 56299.09 51017.53 55844.22 63467.84 67274.23 53548.64 44138.91 42166.49 
##      498      499      500      501      502      503      504      505 
## 42489.26 43279.38 37773.37 40169.14 45463.66 51137.79 51846.53 51956.95 
##      506      507      508      509      510      511      512      513 
## 45667.18 47005.39 43269.53 46021.36 45687.49 39411.55 40541.14 39717.38 
##      514      515      516      517      518      519      520      521 
## 40821.55 43458.09 36307.67 31593.27 55451.40 63601.31 67249.43 60637.25 
##      522      523      524      525      526      527      528      529 
## 61974.97 75542.65 82510.13 57493.49 52370.32 49069.04 53442.21 52949.20 
##      530      531      532      533      534      535      536      537 
## 43145.78 48130.99 60774.71 55302.85 58695.99 62678.47 59734.98 54772.33 
##      538      539      540      541      542      543      544 
## 48243.28 46900.95 54827.53 54578.29 47148.21 49368.88 49195.78 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.843
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original     bias    std. error
## t1*    5.781151  0.5680425    3.225872
## t2* 1709.001856 27.3529550  237.585484
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value   Upper CI
## 1 interrupt_var    1.897616       5.929641   12.16758
## 2    lag_depvar 1371.031207    1722.038246 2149.36242

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
   dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"electrodomésticos/mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
                                            gasto=="Chromecast"~"electrodomésticos/mantención casa",
                                            gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"electrodomésticos/mantención casa",
                                            gasto=="Sopapo"~"electrodomésticos/mantención casa",
                                            gasto=="filtro agua"~"electrodomésticos/mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Transporte",
                                            gasto=="Uber Reñaca"~"Transporte",
                                            gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
                                            gasto=="Aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
                                            gasto=="Pila estufa"~"electrodomésticos/mantención casa",
                                            gasto=="Reloj"~"electrodomésticos/mantención casa",
                                            gasto=="Arreglo"~"electrodomésticos/mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +

  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03"))))

 # scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start = 
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")
library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
## =-=-=-=-= Iteration 0 Mon Jan 30 00:37:55 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Jan 30 00:38:05 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Jan 30 00:38:15 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Jan 30 00:38:25 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Jan 30 00:38:35 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Jan 30 00:38:45 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Jan 30 00:38:55 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Jan 30 00:39:05 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Jan 30 00:39:15 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Jan 30 00:39:25 2023
##  =-=-=-=-=
#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
  dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Otros",
                                            gasto=="Uber Reñaca"~"Otros",
                                            gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021|2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("202",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_23 %>% 
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))
Item 2023 2022 2021 2020
Agua NA 5.410333 5.629750 7.065750
Comida NA 310.278417 314.087500 340.369556
Comunicaciones NA 0.000000 0.000000 0.000000
Electricidad NA 47.072333 38.297667 32.399972
Enceres NA 20.086417 17.443792 24.633194
Farmacia NA 1.831667 7.913875 9.954833
Gas/Bencina NA 44.325000 28.954333 25.055667
Diosi NA 31.180667 41.934250 40.329944
donaciones/regalos NA 0.000000 7.170083 7.631083
Electrodomésticos/ Mantención casa NA 3.944000 30.269500 23.040778
VTR NA 25.156667 22.121792 21.119111
Netflix NA 7.151583 7.090167 7.475861
Otros NA 3.151083 1.575542 1.050361
Total 0 499.588167 522.488250 540.126111
## Joining with `by = join_by(word)`


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")

tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
##   = T)`.
## Caused by warning:
## !  41 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:1866, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)


fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)

La proyección de la UF a 298 días más 2023-02-09 00:04:58 sería de: 35.622 pesos// Percentil 95% más alto proyectado: 38.372,7

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 35321.25 35319.75
Lo.80 35326.97 35323.95
Point.Forecast 35621.81 36334.38
Hi.80 37175.13 40926.52
Hi.95 38024.65 43357.46


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(1,0,0) with non-zero mean 
## 
## Coefficients:
##          ar1      mean
##       0.3174  997.7521
## s.e.  0.1442   34.6825
## 
## sigma^2 = 27808:  log likelihood = -306.2
## AIC=618.4   AICc=618.96   BIC=623.95
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(1,0,0) errors 
## 
## Coefficients:
##          ar1     xreg
##       0.3619  32.9891
## s.e.  0.1461   1.2578
## 
## sigma^2 = 29026:  log likelihood = -307.22
## AIC=620.45   AICc=621   BIC=626
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 896.1947 653.0927 710.8832
Lo.80 1020.1808 772.3914 795.2986
Point.Forecast 1254.3961 997.7519 983.0669
Hi.80 1488.6114 1223.1123 1281.8781
Hi.95 1612.5975 1342.4110 1475.2320


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 55 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Andrés, Tami
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Andrés Tami
1 marzo_2019 68268 175533
2 abril_2019 55031 152640
3 mayo_2019 192219 152985
4 junio_2019 84961 291067
5 julio_2019 205893 241389


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] CausalImpact_1.3.0  bsts_0.9.9          BoomSpikeSlab_1.2.5
##  [4] Boom_0.9.11         scales_1.2.1        ggiraph_0.8.6      
##  [7] tidytext_0.4.1      DT_0.27             autoplotly_0.1.4   
## [10] rvest_1.0.3         plotly_4.10.1       xts_0.12.2         
## [13] forecast_8.20       wordcloud_2.6       RColorBrewer_1.1-3 
## [16] SnowballC_0.7.0     tm_0.7-10           NLP_0.2-1          
## [19] tsibble_1.1.3       forcats_1.0.0       dplyr_1.1.0        
## [22] purrr_1.0.1         tidyr_1.3.0         tibble_3.1.8       
## [25] ggplot2_3.4.0       tidyverse_1.3.2     sjPlot_2.8.12      
## [28] lattice_0.20-45     gridExtra_2.3       plotrix_3.8-2      
## [31] sparklyr_1.7.9      httr_1.4.4          readxl_1.4.1       
## [34] zoo_1.8-11          stringr_1.5.0       stringi_1.7.12     
## [37] DataExplorer_0.8.2  data.table_1.14.6   reshape2_1.4.4     
## [40] fUnitRoots_4021.80  plyr_1.8.8          readr_2.1.3        
## 
## loaded via a namespace (and not attached):
##   [1] utf8_1.2.2          tidyselect_1.2.0    lme4_1.1-31        
##   [4] htmlwidgets_1.6.1   munsell_0.5.0       codetools_0.2-18   
##   [7] its.analysis_1.6.0  withr_2.5.0         colorspace_2.1-0   
##  [10] ggfortify_0.4.15    highr_0.10          knitr_1.42         
##  [13] uuid_1.1-0          rstudioapi_0.14     TTR_0.24.3         
##  [16] labeling_0.4.2      emmeans_1.8.4-1     slam_0.1-50        
##  [19] bit64_4.0.5         farver_2.1.1        datawizard_0.6.5   
##  [22] fBasics_4021.93     rprojroot_2.0.3     vctrs_0.5.2        
##  [25] generics_0.1.3      xfun_0.36           timechange_0.2.0   
##  [28] R6_2.5.1            bitops_1.0-7        cachem_1.0.6       
##  [31] assertthat_0.2.1    networkD3_0.4       vroom_1.6.1        
##  [34] nnet_7.3-16         googlesheets4_1.0.1 gtable_0.3.1       
##  [37] spatial_7.3-14      timeDate_4022.108   rlang_1.0.6        
##  [40] forge_0.2.0         systemfonts_1.0.4   splines_4.1.2      
##  [43] lazyeval_0.2.2      gargle_1.2.1        selectr_0.4-2      
##  [46] broom_1.0.3         yaml_2.3.7          abind_1.4-5        
##  [49] modelr_0.1.10       crosstalk_1.2.0     backports_1.4.1    
##  [52] quantmod_0.4.20     tokenizers_0.3.0    tools_4.1.2        
##  [55] ellipsis_0.3.2      gplots_3.1.3        jquerylib_0.1.4    
##  [58] Rcpp_1.0.10         base64enc_0.1-3     fracdiff_1.5-2     
##  [61] haven_2.5.1         fs_1.5.2            magrittr_2.0.3     
##  [64] timeSeries_4021.105 lmtest_0.9-40       reprex_2.0.2       
##  [67] googledrive_2.0.0   mvtnorm_1.1-3       sjmisc_2.8.9       
##  [70] hms_1.1.2           evaluate_0.20       xtable_1.8-4       
##  [73] sjstats_0.18.2      ggeffects_1.1.5     compiler_4.1.2     
##  [76] KernSmooth_2.23-20  crayon_1.5.2        minqa_1.2.5        
##  [79] htmltools_0.5.4     tzdb_0.3.0          lubridate_1.9.1    
##  [82] DBI_1.1.3           sjlabelled_1.2.0    dbplyr_2.3.0       
##  [85] MASS_7.3-54         boot_1.3-28         Matrix_1.5-3       
##  [88] car_3.1-1           cli_3.6.0           quadprog_1.5-8     
##  [91] parallel_4.1.2      insight_0.18.8      igraph_1.3.5       
##  [94] pkgconfig_2.0.3     xml2_1.3.3          bslib_0.4.2        
##  [97] estimability_1.4.1  anytime_0.3.9       snakecase_0.11.0   
## [100] janeaustenr_1.0.0   digest_0.6.31       janitor_2.1.0      
## [103] rmarkdown_2.20      cellranger_1.1.0    curl_5.0.0         
## [106] gtools_3.9.4        urca_1.3-3          nloptr_2.0.3       
## [109] lifecycle_1.0.3     nlme_3.1-153        jsonlite_1.8.4     
## [112] tseries_0.10-52     carData_3.0-5       viridisLite_0.4.1  
## [115] fansi_1.0.4         pillar_1.8.1        fastmap_1.1.0      
## [118] glue_1.6.2          bayestestR_0.13.0   bit_4.0.5          
## [121] sass_0.4.5          performance_0.10.2  r2d3_0.2.6         
## [124] caTools_1.18.2
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))